scholarly journals Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms

2020 ◽  
Vol 17 (8) ◽  
pp. 2749 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ataollah Shirzadi ◽  
Himan Shahabi ◽  
Sushant K. Singh ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Landslide Susceptibility ◽  
Logistic Model ◽  
Naive Bayes ◽  
Shallow Landslide ◽  
Naïve Bayes ◽  
Support Vector ◽  
Model Tree ◽  
Logistic Model Tree

Shallow landslides damage buildings and other infrastructure, disrupt agriculture practices, and can cause social upheaval and loss of life. As a result, many scientists study the phenomenon, and some of them have focused on producing landslide susceptibility maps that can be used by land-use managers to reduce injury and damage. This paper contributes to this effort by comparing the power and effectiveness of five machine learning, benchmark algorithms—Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine—in creating a reliable shallow landslide susceptibility map for Bijar City in Kurdistan province, Iran. Twenty conditioning factors were applied to 111 shallow landslides and tested using the One-R attribute evaluation (ORAE) technique for modeling and validation processes. The performance of the models was assessed by statistical-based indexes including sensitivity, specificity, accuracy, mean absolute error (MAE), root mean square error (RMSE), and area under the receiver operatic characteristic curve (AUC). Results indicate that all the five machine learning models performed well for shallow landslide susceptibility assessment, but the Logistic Model Tree model (AUC = 0.932) had the highest goodness-of-fit and prediction accuracy, followed by the Logistic Regression (AUC = 0.932), Naïve Bayes Tree (AUC = 0.864), ANN (AUC = 0.860), and Support Vector Machine (AUC = 0.834) models. Therefore, we recommend the use of the Logistic Model Tree model in shallow landslide mapping programs in semi-arid regions to help decision makers, planners, land-use managers, and government agencies mitigate the hazard and risk.

scholarly journals A Rough Set and Cellular Genetic Fusion Algorithm for Acute Critical Disease Prediction

2020 ◽  
Vol 15 (6) ◽  
Author(s):  
Hongxin Wang ◽  
Lijing Jia ◽  
Heng Zhuang ◽  
Xueyan Li ◽  
Yuzhuo Zhao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Rough Set ◽  
Rough Set Theory ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Disease Prediction ◽  
Fusion Algorithm

This study is to solve the problems of an overly-broad scale of medical indicators, lack of retrospective research samples, insufficient depth of data mining, and low disease prediction accuracy. In this paper, we propose an intelligent screening algorithm that combines a genetic algorithm, cellular automata, and rough set theory. This algorithm can achieve high accuracy in predicting patient outcomes with a small number of indicators. And we compare it with the traditional genetic algorithm. We built the prediction model with 64 indicators based on the logistic regression (AUC 0.8628), support vector machine (AUC 0.5319), Naïve Bayes (AUC 0.7102), and AdaBoost algorithms (AUC 0.9095). Using the cellular genetic algorithm for attribute screening not only effectively reduces the number of indicators but also achieve almost the same accuracy of prediction with 8 indicators based on the logistic regression (AUC 0.8782), support vector machine (AUC 0.8525), Naïve Bayes (AUC 0.8408), and AdaBoost algorithms (AUC 0.8770). Compared with the traditional scoring system, the predictive model established in this paper can more accurately predict rebleeding accidents based on physiological test indicators and continuous patient indicators.

scholarly journals Model Prediksi Prestasi Mahasiswa Berdasarkan Evaluasi Pembelajaran Menggunakan Pendekatan Data Science

2021 ◽  
Vol 1 (1) ◽  
pp. 14-20
Author(s):  
Tommy Tommy ◽  
Amir Mahmud Husein
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Data Science ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
Support Vector ◽  
K Nearest Neighbors

Perguruan tinggi merupakan satuan penyelenggara pendidikan tinggi sebagai tingkat lanjut jenjang pendidikan menengah di jalur pendidikan formal. Aspek prestasi belajar merupakan salah satu aspek penilaian keberhasilan perguruan tinggi dalam proses belajar. Dalam makalah ini menyajikan hasil analisis hubungan antara pembelajaran dengan prestasi mahasiswa dimana tahapan yang dilakukan menggunakan pendetakan data science. Berdasarkan Analisis data terdapat tiga indikator penting dalam penilaian prestasi belajar yaitu pedagogi, profesional dan kepribadian. Ketiga fitur digunakan sebagai variabel dependen untuk memprediksi prestasi belajar dimana algoritma DecisionTree menghasilkan akurasi lebih baik dari pada model k-nearest neighbors (KNN), Logistic Regression, Support Vector Machine, Naive Bayes dan dengan tingkat akurasi 68%, kemudian KNN dengan akurasi 66% dan lainnya sebesar 55% pada masing-masing algoritma yang diusulkan.

scholarly journals Landslide Detection and Susceptibility Modeling on Cameron Highlands (Malaysia): A Comparison between Random Forest, Logistic Regression and Logistic Model Tree Algorithms

Forests ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 830 ◽  
Author(s):  
Viet-Ha Nhu ◽  
Ayub Mohammadi ◽  
Himan Shahabi ◽  
Baharin Bin Ahmad ◽  
Nadhir Al-Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Logistic Regression ◽  
Random Forest ◽  
Landslide Susceptibility ◽  
Predictive Value ◽  
Logistic Model ◽  
Model Tree ◽  
Cameron Highlands ◽  
Logistic Model Tree

We used remote sensing techniques and machine learning to detect and map landslides, and landslide susceptibility in the Cameron Highlands, Malaysia. We located 152 landslides using a combination of interferometry synthetic aperture radar (InSAR), Google Earth (GE), and field surveys. Of the total slide locations, 80% (122 landslides) were utilized for training the selected algorithms, and the remaining 20% (30 landslides) were applied for validation purposes. We employed 17 conditioning factors, including slope angle, aspect, elevation, curvature, profile curvature, stream power index (SPI), topographic wetness index (TWI), lithology, soil type, land cover, normalized difference vegetation index (NDVI), distance to river, distance to fault, distance to road, river density, fault density, and road density, which were produced from satellite imageries, geological map, soil maps, and a digital elevation model (DEM). We used these factors to produce landslide susceptibility maps using logistic regression (LR), logistic model tree (LMT), and random forest (RF) models. To assess prediction accuracy of the models we employed the following statistical measures: negative predictive value (NPV), sensitivity, positive predictive value (PPV), specificity, root-mean-squared error (RMSE), accuracy, and area under the receiver operating characteristic (ROC) curve (AUC). Our results indicated that the AUC was 92%, 90%, and 88% for the LMT, LR, and RF algorithms, respectively. To assess model performance, we also applied non-parametric statistical tests of Friedman and Wilcoxon, where the results revealed that there were no practical differences among the used models in the study area. While landslide mapping in tropical environment such as Cameron Highlands remains difficult, the remote sensing (RS) along with machine learning techniques, such as the LMT model, show promise for landslide susceptibility mapping in the study area.

scholarly journals Comparison of Tree Method, Support Vector Machine, Naïve Bayes, and Logistic Regression on Coffee Bean Image

2021 ◽  
Vol 9 (1) ◽  
pp. 126-136
Author(s):  
Rahmat Robi Waliyansyah ◽  
Umar Hafidz Asy'ari Hasbullah
Keyword(s):  
Support Vector Machine ◽  
Logistic Regression ◽  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Classification Model ◽  
Support Vector ◽  
Coffee Bean ◽  
Coffee Beans ◽  
The Many

Coffee is one of the many favorite drinks of Indonesians. In Indonesia there are 2 types of coffee, namely Arabica & Robusta. The classification of coffee beans is usually done in a traditional way & depends on the human senses. However, the human senses are often inconsistent, because it depends on the mental or physical condition in question at that time, and only qualitative measures can be determined. In this study, to classify coffee beans is done by digital image processing. The parameters used are texture analysis using the Gray Level Coocurrence Matrix (GLCM) method with 4 features, namely Energy, Correlation, Homogeneity & Contrast. For feature extraction using a classification algorithm, namely Naïve Bayes, Tree, Support Vector Machine (SVM) and Logistic Regression. The evaluation of the coffee bean classification model uses the following parameters: AUC, F1, CA, precision & recall. The dataset used is 29 images of Arabica coffee beans and 29 images of Robusta beans. To test the accuracy of the model using Cross Validation. The results obtained will be evaluated using the confusion Matrix. Based on the results of testing and evaluation of the model, it is obtained that the SVM method is the best with the value of AUC = 1, CA = 0.983, F1 = 0.983, Precision = 0.983 and Recall = 0.983.

scholarly journals Comparison of Support Vector Machine, Naïve Bayes and Logistic Regression for Assessing the Necessity for Coronary Angiography

2020 ◽  
Vol 17 (18) ◽  
pp. 6449
Author(s):  
Parastoo Golpour ◽  
Majid Ghayour-Mobarhan ◽  
Azadeh Saki ◽  
Habibollah Esmaily ◽  
Ali Taghipour ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Coronary Angiography ◽  
Naive Bayes ◽  
Area Under The Curve ◽  
Naïve Bayes ◽  
Support Vector ◽  
Bayes Model

(1) Background: Coronary angiography is considered to be the most reliable method for the diagnosis of cardiovascular disease. However, angiography is an invasive procedure that carries a risk of complications; hence, it would be preferable for an appropriate method to be applied to determine the necessity for angiography. The objective of this study was to compare support vector machine, naïve Bayes and logistic regressions to determine the diagnostic factors that can predict the need for coronary angiography. These models are machine learning algorithms. Machine learning is considered to be a branch of artificial intelligence. Its aims are to design and develop algorithms that allow computers to improve their performance on data analysis and decision making. The process involves the analysis of past experiences to find practical and helpful regularities and patterns, which may also be overlooked by a human. (2) Materials and Methods: This cross-sectional study was performed on 1187 candidates for angiography referred to Ghaem Hospital, Mashhad, Iran from 2011 to 2012. A logistic regression, naive Bayes and support vector machine were applied to determine whether they could predict the results of angiography. Afterwards, the sensitivity, specificity, positive and negative predictive values, AUC (area under the curve) and accuracy of all three models were computed in order to compare them. All analyses were performed using R 3.4.3 software (R Core Team; Auckland, New Zealand) with the help of other software packages including receiver operating characteristic (ROC), caret, e1071 and rminer. (3) Results: The area under the curve for logistic regression, naïve Bayes and support vector machine were similar—0.76, 0.74 and 0.75, respectively. Thus, in terms of the model parsimony and simplicity of application, the naïve Bayes model with three variables had the best performance in comparison with the logistic regression model with seven variables and support vector machine with six variables. (4) Conclusions: Gender, age and fasting blood glucose (FBG) were found to be the most important factors to predict the result of coronary angiography. The naïve Bayes model performed well using these three variables alone, and they are considered important variables for the other two models as well. According to an acceptable prediction of the models, they can be used as pragmatic, cost-effective and valuable methods that support physicians in decision making.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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